1. Field of the Invention
The present invention relates to a method for estimating channel frequency response (CFR) for orthogonal frequency division multiplexing (OFDM) communications, and more particularly to a CFR estimation method for multi-band OFDM-based ultra-wideband (UWB) systems.
2. Description of Related Art
OFDM-based UWB communication has attracted a lot of attention in recent years, as described in the following references: [1] “A. Batra, J. Balakrishnan, G. R. Aiello, J. R. Foerster, and A. Dabak, “Design of a multiband OFDM system for realistic UWB channel environments,” IEEE Trans. Microwave Theory and Techniques, vol. 52, no. 9, pp. 2123-2138, September 2004.”; [2] “WiMedia MBOA, MultiBand OFDM Physical Layer Specification, ver. 1.1.5, Jul. 14, 2006.”; [3] “Y. Li, A. F. Molisch, and J. Zhang “Practical approaches to channel estimation and interference suppression for OFDM-Based UWB communications,” IEEE Trans. Wireless Commun., vol. 5, no. 9, pp. 2317-2320, September 2006.”. The large bandwidth occupancy of UWB (from 3.1 GHz to 10.6 GHz) and the high efficiency in spectrum utilization provided by OFDM make it possible for the OFDM-UWB technology to achieve very high channel capacity. The OFDM-UWB can provide low-cost and high-speed wireless connectivity among devices within a short range. The wireless universal serial bus (USB), for example, has adopted the OFDM-UWB radio layer with the data rate up to 480 Mbps.
The extremely wide-band processing has brought a lot of challenges to the OFDM-UWB system design, especially to the design of some crucial receiving modules such as time synchronization, frequency synchronization, as well as the channel frequency response (CFR) estimation. The OFDM-based UWB system, as specified by the Wimedia Alliance (shown in [2]), uses frame-based transmission. Usually, the UWB channel can be treated as invariant over the transmission period of one OFDM frame. The estimation of CFR thus can be done based on the dedicated channel estimation sequence included in the frame preamble. In this sense, many existing schemes including the least-square (LS), the maximum-likelihood (ML), and the minimum mean-squared error (MMSE) algorithms can be adopted for CFR estimation, as described in the following references: [4] “B. Muquet, M. de Courville, and P. Duhamel, “Subspace-based blind and semi-blind channel estimation for OFDM systems,” IEEE Trans. Signal Proc., vol. 50, no. 7, pp. 1699-1712, July 2002.”; [5] “S. Zhou and G. B. Giannakis, “Finite-Alphabet based channel estimation for OFDM and related multicarrier systems,” IEEE Trans. Commun., vol. 49, no. 8, pp. 1402-1414, August 2001.”; [6] “M. Morelli and U. Mengali, “A comparison of pilot-aided channel estimation methods for OFDM systems,” IEEE Trans. Signal Processing, vol. 49, no. 12, pp. 3065-3073, December 2001.”; [7] “O. Edfors, M. Sandell, J. van de Beek, S. K. Wilson, and P. O. Börjesson, “OFDM channel estimation by singular value decomposition,” IEEE Trans. Commun., vol. 46, no. 7, pp. 931-939, July 1998.”; [8] “L. Deneire, P. Vandenameele, L. V. d. Perre, B. Gyselinckx, and M. Engels, “A low complexity ML channel estimator for OFDM,” IEEE Trans. Commun., vol. 51, no. 2, pp. 135-140, February 2003.”. LS is the simplest, but has the drawback of low noise reduction capability. In particular, as the OFDM-UWB is supposed to deliver good service even under very low signal-noise ratio (SNR) condition (≦0 dB, see [1]), simply applying the LS algorithm to the channel estimation sequence may not yield the CFR estimation with acceptable accuracy. Both ML and MMSE offer high estimation accuracy, but suffer from high computational complexity. The ML estimation introduced in [8], for example, either requires pre-storing a large matrix in memory or performing matrix inversion in real time. This, of course, is prohibitive for actual implementation of low-power and low-cost wireless USB devices.